by Team Diffgram | Mar 30, 2022 | Updates
After examining ten of the frequently cited datasets for testing machine learning systems, MIT Computer researchers have found out that these data sets have critical labeling errors. These errors could cause deep problems for AI systems developed using them. The...
by Team Diffgram | Feb 22, 2022 | Training Data 101
The quality of your annotations and training data depends on how well your annotation workforce executes the tasks. Guides are readable instructions that help improve the quality of annotations created by your labelers. Diffgram allows you to create detailed guides...
by Team Diffgram | Feb 22, 2022 | Training Data 101
You can better leverage Diffgram when your team perfectly knows how to label data for machine learning efforts. Diffgram’s data labeling tool allows you to effectively manage your data labeling tasks without having to leave the platform. No more isolated tools....
by Team Diffgram | Feb 15, 2022 | Training Data 101
To annotate your text training data on Diffgram, you first need to create a project. Here is a step-by-step guide to help you get started with your training data labeling on Diffgram. The process is standard and the same across all different data types viz Image,...
by Team Diffgram | Feb 10, 2022 | Training Data 101
The standard was to manually export your data, then write a script to feed the data to your models for training. Today we are changing that with the all-new: Diffgram Streaming — Direct to Memory for Pytorch and Tensorflow This is huge! But before we get...
